Magnetotelluric (MT) inversion models are widely used to interpret crustal conductivity structures related to fluid pathways, deformation zones, and mineralization processes; however, evaluating the reliability of inversion-derived anomalies remains challenging when datasets contain subtle station-dependent distortions or residual cultural noise. We present a jackknife-inspired, model-space diagnostic framework based on leave-one-station-out (LOSO) inversion to quantify station influence and improve interpretation reliability. The workflow consists of (1) generating a LOSO inversion ensemble using identical inversion settings, (2) computing ensemble statistics and standardized perturbation metrics to identify sensitive zones, and (3) applying distribution-based diagnostics to classify station influence and guide construction of an ensemble-refined model. Synthetic experiments demonstrate that the framework distinguishes localized station-controlled artifacts from broadly supported structural responses, allowing targeted correction without altering robust features. Application to a field MT dataset acquired in a noise-affected environment shows that a mid-crustal conductive structure remains stable across the LOSO ensemble, while some shallow anomalies exhibit strong station dependence. The resulting ensemble-refined model introduces only localized modifications, demonstrating that ensemble-based model-space diagnostics provide a practical and reproducible strategy for validating MT inversion results and improving confidence in exploration-oriented conductivity interpretations.
Feng et al. (2026) studied this question.